The Lasting Legacy of Metabolic Conditions in Hepatitis C: Association Between Phenotypes of Metabolic Dysfunction-Associated Steatotic Liver Disease and Liver Fibrosis
Bibliographic record
Abstract
Hepatitis C virus (HCV) infection is a leading cause of chronic liver disease worldwide. While antiviral therapy achieves sustained virologic response (SVR) in over 95% of patients, metabolic dysfunction continues to influence liver fibrosis progression. Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is increasingly recognized as a significant factor in liver health, yet its impact on fibrosis in individuals with a history of HCV infection remains underexplored. This thesis is centered on a cross-sectional study investigating the association between MASLD and significant liver fibrosis in a cohort of 590 individuals with history of chronic HCV infection from two Canadian academic centres. Liver fibrosis was assessed using innovative non-invasive imaging markers, and MASLD phenotypes were defined based on established metabolic risk factors. Unadjusted and adjusted logistic regression models were used to evaluate the relationship between MASLD phenotypes and significant fibrosis, controlling for potential confounders. The findings indicate that MASLD is strongly associated with significant liver fibrosis (adjusted odds ratio [aOR] 2.29, 95% confidence interval [CI] 1.07–4.87), with diabetic, hypertensive, and overweight MASLD phenotypes exhibiting the highest association in patients with HCV (aORs of 4.76 (95% CI 2.16–10.49), 3.44 (95% CI 1.77–6.68) and 2.54 (95% CI 1.27–5.07), respectively.) These results underscore the persistent role of metabolic dysfunction in liver disease progression, independent of viral eradication. The study highlights the necessity for an integrated, multidisciplinary approach in post-SVR management of individuals with history of chronic HCV infection that prioritizes metabolic health to mitigate fibrosis risk and optimize long-term liver outcomes
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".